Gastos Casa
01 March, 2025
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 20/12/2024 | Plata basureros | 10000 | Tami | NA |
| 3/12/2024 | Agua | 15828 | Andrés | PAC AGUAS ANDIN 000000005687837 |
| 22/12/2024 | Comida | 46608 | Tami | Supermercado |
| 10/12/2024 | Otros | 47484 | Andrés | viaje brasil |
| 29/12/2024 | Electricidad | 27417 | Andrés | NA |
| 29/12/2024 | Comida | 83284 | Tami | Supermercado |
| 30/12/2024 | Comida | 30000 | Andrés | nueces almendras mix etc |
| 30/12/2024 | Otros | 47484 | Andrés | viaje a brasil (duplicar para q cargue sobre tami) |
| 4/1/2025 | Diosi | 53999 | Andrés | n y d pumpkin 7.5 |
| 4/1/2025 | Comida | 15260 | Andrés | NA |
| 6/1/2025 | Comida | 40988 | Tami | Supermercado |
| 8/1/2025 | Pago cámaras MB | 20000 | Tami | NA |
| 13/1/2025 | Comida | 67387 | Tami | Supermercado |
| 20/1/2025 | Comida | 21692 | Andrés | gnoccis |
| 20/1/2025 | Comida | 86884 | Tami | Supermercado |
| 21/1/2025 | Comida | 21525 | Andrés | piwen |
| 23/1/2025 | VTR | 21990 | Andrés | NA |
| 25/1/2025 | Diosi | 20000 | Andrés | arena diosi |
| 27/1/2025 | Comida | 71516 | Tami | Supermercado |
| 30/1/2025 | Electricidad | 55000 | Andrés | NA |
| 6/2/2025 | Comida | 52730 | Andrés | supermercado (no cobre el otro de 25k pq muchas son cosas mías) |
| 9/2/2025 | Comida | 12500 | Andrés | NA |
| 17/2/2025 | Comida | 7940 | Andrés | NA |
| 18/2/2025 | Electricidad | 64888 | Andrés | la puse por adelantado para que no se me olvide |
| 18/2/2025 | Comida | 17820 | Tami | Supermercado |
| 23/2/2025 | Comida | 86908 | Tami | Supermercado |
| 27/2/2025 | Comida | 10000 | Andrés | NA |
| 26/2/2025 | Comida | 4620 | Andrés | NA |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 9.7913e+08 2 4.9311 0.0074 **
## lag_depvar 2.6171e+11 1 2636.0276 <2e-16 ***
## Residuals 8.0716e+10 813
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 -1922.597 16380.27 0.1527337
## 2-0 31477.066 23235.055 39719.08 0.0000000
## 2-1 24248.228 19469.603 29026.85 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 619 59609.00 2 67061.57
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## 636 75716.00 2 81753.00
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## 658 44454.57 2 55429.43
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## 660 52126.71 2 47184.00
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## 673 44051.86 2 46624.71
## 674 35852.86 2 44051.86
## 675 29737.71 2 35852.86
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## 678 38298.57 2 32881.71
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## 680 38601.86 2 40886.14
## 681 38628.86 2 38601.86
## 682 39142.57 2 38628.86
## 683 32666.14 2 39142.57
## 684 39911.57 2 32666.14
## 685 39336.29 2 39911.57
## 686 39678.86 2 39336.29
## 687 41963.14 2 39678.86
## 688 54220.57 2 41963.14
## 689 63901.86 2 54220.57
## 690 73116.00 2 63901.86
## 691 60863.86 2 73116.00
## 692 56293.86 2 60863.86
## 693 52725.00 2 56293.86
## 694 58625.00 2 52725.00
## 695 47513.00 2 58625.00
## 696 40300.14 2 47513.00
## 697 33312.43 2 40300.14
## 698 29556.71 2 33312.43
## 699 27816.71 2 29556.71
## 700 34120.29 2 27816.71
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## 702 32902.57 2 32132.57
## 703 39694.14 2 32902.57
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## 706 99637.71 2 79551.14
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## 708 98395.14 2 95424.29
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## 710 114267.57 2 115594.71
## 711 88353.29 2 114267.57
## 712 88750.86 2 88353.29
## 713 78835.71 2 88750.86
## 714 75519.14 2 78835.71
## 715 73202.86 2 75519.14
## 716 53433.29 2 73202.86
## 717 48165.71 2 53433.29
## 718 52163.14 2 48165.71
## 719 49306.86 2 52163.14
## 720 36846.86 2 49306.86
## 721 43220.57 2 36846.86
## 722 38952.29 2 43220.57
## 723 41522.29 2 38952.29
## 724 39090.00 2 41522.29
## 725 28452.57 2 39090.00
## 726 32975.00 2 28452.57
## 727 33690.71 2 32975.00
## 728 26405.29 2 33690.71
## 729 47087.43 2 26405.29
## 730 49660.29 2 47087.43
## 731 47409.71 2 49660.29
## 732 53881.71 2 47409.71
## 733 45189.57 2 53881.71
## 734 45503.86 2 45189.57
## 735 54640.14 2 45503.86
## 736 39131.29 2 54640.14
## 737 35024.14 2 39131.29
## 738 44755.43 2 35024.14
## 739 41063.29 2 44755.43
## 740 42783.29 2 41063.29
## 741 45952.57 2 42783.29
## 742 44937.43 2 45952.57
## 743 40838.43 2 44937.43
## 744 48838.43 2 40838.43
## 745 43139.14 2 48838.43
## 746 67134.29 2 43139.14
## 747 73224.29 2 67134.29
## 748 68770.71 2 73224.29
## 749 59539.29 2 68770.71
## 750 82179.86 2 59539.29
## 751 74252.14 2 82179.86
## 752 73015.00 2 74252.14
## 753 56116.43 2 73015.00
## 754 111885.00 2 56116.43
## 755 131425.14 2 111885.00
## 756 136678.00 2 131425.14
## 757 115531.29 2 136678.00
## 758 118310.86 2 115531.29
## 759 117449.43 2 118310.86
## 760 115193.57 2 117449.43
## 761 61025.43 2 115193.57
## 762 43913.86 2 61025.43
## 763 46099.29 2 43913.86
## 764 44524.86 2 46099.29
## 765 42208.71 2 44524.86
## 766 166486.57 2 42208.71
## 767 171565.29 2 166486.57
## 768 200415.71 2 171565.29
## 769 204498.14 2 200415.71
## 770 197558.86 2 204498.14
## 771 195266.57 2 197558.86
## 772 203144.29 2 195266.57
## 773 85493.71 2 203144.29
## 774 74721.57 2 85493.71
## 775 36232.14 2 74721.57
## 776 40161.71 2 36232.14
## 777 40629.86 2 40161.71
## 778 45663.71 2 40629.86
## 779 39252.29 2 45663.71
## 780 39618.57 2 39252.29
## 781 39438.43 2 39618.57
## 782 44650.71 2 39438.43
## 783 38626.71 2 44650.71
## 784 38280.43 2 38626.71
## 785 44134.14 2 38280.43
## 786 47596.43 2 44134.14
## 787 45598.43 2 47596.43
## 788 42564.29 2 45598.43
## 789 45699.14 2 42564.29
## 790 49553.86 2 45699.14
## 791 50018.43 2 49553.86
## 792 43772.86 2 50018.43
## 793 39235.43 2 43772.86
## 794 39905.00 2 39235.43
## 795 40374.43 2 39905.00
## 796 34230.57 2 40374.43
## 797 34324.14 2 34230.57
## 798 33491.57 2 34324.14
## 799 33366.43 2 33491.57
## 800 46646.86 2 33366.43
## 801 49770.86 2 46646.86
## 802 57339.86 2 49770.86
## 803 59799.14 2 57339.86
## 804 53577.14 2 59799.14
## 805 61775.29 2 53577.14
## 806 70627.86 2 61775.29
## 807 57888.43 2 70627.86
## 808 49960.71 2 57888.43
## 809 42923.71 2 49960.71
## 810 47284.86 2 42923.71
## 811 52284.86 2 47284.86
## 812 50191.00 2 52284.86
## 813 36465.86 2 50191.00
## 814 34525.14 2 36465.86
## 815 43199.14 2 34525.14
## 816 52757.43 2 43199.14
## 817 43200.86 2 52757.43
## 818 36772.29 2 43200.86
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 661 53711.33 22394.025
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57 61025.43 43913.86 46099.29 44524.86
## [764] 42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29 85493.71 74721.57 36232.14 40161.71 40629.86 45663.71
## [778] 39252.29 39618.57 39438.43 44650.71 38626.71 38280.43 44134.14
## [785] 47596.43 45598.43 42564.29 45699.14 49553.86 50018.43 43772.86
## [792] 39235.43 39905.00 40374.43 34230.57 34324.14 33491.57 33366.43
## [799] 46646.86 49770.86 57339.86 59799.14 53577.14 61775.29 70627.86
## [806] 57888.43 49960.71 42923.71 47284.86 52284.86 50191.00 36465.86
## [813] 34525.14 43199.14 52757.43 43200.86 36772.29
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [815] 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6
## 2023.122121 4042.187107 -539.838711 2436.539874 -2973.125581
## 7 8 9 10 11
## 517.476134 -5657.715493 -1185.589480 -3963.685033 -413.203966
## 12 13 14 15 16
## -4935.619269 -1602.455825 -892.826655 383.852333 -3237.935154
## 17 18 19 20 21
## -371.479808 -2124.552244 6610.249493 -1529.397968 -1207.720870
## 22 23 24 25 26
## 1476.626639 -1187.256148 234.578692 1694.377469 -7104.256425
## 27 28 29 30 31
## 950.754768 8194.212156 413.336096 -18.817655 -2405.047750
## 32 33 34 35 36
## 1573.860291 4568.999750 1120.136306 2384.429159 -1876.178482
## 37 38 39 40 41
## 4601.964129 4300.240249 -2281.753682 -2984.985304 -1110.983206
## 42 43 44 45 46
## -10741.354235 7297.590289 2558.844930 1366.250970 8103.577011
## 47 48 49 50 51
## 678.574359 6521.606779 6703.167405 -5897.258899 -4803.991705
## 52 53 54 55 56
## -5063.544250 -7928.044099 6136.308175 -4075.644717 -4890.402453
## 57 58 59 60 61
## 3862.932251 891.062174 -30.020595 144.035839 -4995.001160
## 62 63 64 65 66
## 18131.810578 3631.185200 -3657.163840 5918.521708 7333.600637
## 67 68 69 70 71
## 14624.252518 1669.380835 -13234.688513 -1315.059115 4636.831454
## 72 73 74 75 76
## -4909.601494 -4408.805259 -10497.655906 2475.074108 -5394.297670
## 77 78 79 80 81
## 1072.948366 -6858.939307 558.881100 -2344.429838 -2683.012816
## 82 83 84 85 86
## -3920.127804 -523.994834 2326.965954 3771.662064 481.706731
## 87 88 89 90 91
## -480.795706 200.215039 4304.873397 -1163.940060 1150.931498
## 92 93 94 95 96
## -2065.352571 -1043.435821 179.148275 275.985331 -7483.232495
## 97 98 99 100 101
## 2398.556199 -8598.431557 -2929.860476 -4027.460048 -1722.660649
## 102 103 104 105 106
## -1247.353033 3194.438577 -2332.778831 2604.112338 -1151.679724
## 107 108 109 110 111
## 977.319145 2592.308562 -3151.953005 -4718.315767 -842.186118
## 112 113 114 115 116
## 1911.343164 11698.857370 -1247.554806 2665.220796 4257.797535
## 117 118 119 120 121
## 3494.838294 -1109.587555 -4723.779103 -3726.651484 2320.684220
## 122 123 124 125 126
## -1733.669135 1341.036823 8857.759762 839.495699 123.181067
## 127 128 129 130 131
## -2527.651505 2651.623263 7047.407915 1002.267384 -8509.062852
## 132 133 134 135 136
## 1747.756681 4132.854739 -3169.629232 -1422.067283 -854.815123
## 137 138 139 140 141
## -3879.994737 1186.290943 -493.564441 -2911.485043 1722.447496
## 142 143 144 145 146
## -1878.716717 -7825.665892 2049.097725 -3472.954797 2110.984609
## 147 148 149 150 151
## -251.590908 1028.337982 -355.548795 1355.726017 1188.563121
## 152 153 154 155 156
## 3357.248080 -4864.000172 -1172.414512 -3233.053231 5961.787184
## 157 158 159 160 161
## 9746.145971 -3634.863739 -4979.511130 3405.586642 -8.323599
## 162 163 164 165 166
## 2491.693491 -6118.621705 -6951.073585 3957.834770 17187.616718
## 167 168 169 170 171
## 3398.045380 -632.180889 -2678.077893 -1332.372974 3364.027201
## 172 173 174 175 176
## -458.952221 -8305.300910 2644.023772 4101.399980 395.689717
## 177 178 179 180 181
## 8520.026726 -9489.690090 -3699.814481 -10968.409600 -11451.134243
## 182 183 184 185 186
## 1035.723522 9088.948081 -1649.416754 5709.509780 6326.000960
## 187 188 189 190 191
## 12917.670547 8169.116094 -4337.966828 2196.372366 10096.277066
## 192 193 194 195 196
## -1931.839946 -2728.219052 -10558.503578 -6623.241145 984.242458
## 197 198 199 200 201
## -5484.626539 -10038.594405 5156.605769 -3305.021297 -1944.712957
## 202 203 204 205 206
## -1034.672380 6263.694257 9637.208301 313.285022 2658.554142
## 207 208 209 210 211
## 2827.100225 5509.170171 12549.998664 -5990.926090 -11584.495074
## 212 213 214 215 216
## -5930.758140 -10839.793278 -5307.865693 1302.360351 -13239.192106
## 217 218 219 220 221
## 16182.636684 7568.979324 1271.809421 26428.965537 12215.855108
## 222 223 224 225 226
## 7004.974488 13689.336857 -4269.859431 -2080.022829 3450.197128
## 227 228 229 230 231
## 33.910011 2428.110716 8690.196177 5509.199697 -2226.570141
## 232 233 234 235 236
## -2135.197758 9129.473607 -11815.538989 -7563.286968 -8804.500883
## 237 238 239 240 241
## -10347.894617 2849.190683 1116.530968 -8535.935160 -9213.897424
## 242 243 244 245 246
## 8884.703127 -7995.834669 2269.474394 -10526.670615 -4262.679503
## 247 248 249 250 251
## 1217.299099 790.194088 -12534.804544 3444.176214 1850.553911
## 252 253 254 255 256
## 3991.136810 1901.837388 -1400.599551 10899.146295 20615.259308
## 257 258 259 260 261
## 2885.327372 -4586.930818 3806.725212 -2003.029733 3436.146932
## 262 263 264 265 266
## -5157.444807 -11185.203482 -4994.517065 -777.387958 -5443.762096
## 267 268 269 270 271
## 8532.472608 -4548.523024 3929.729721 -2377.985156 4163.704050
## 272 273 274 275 276
## 430.312147 7022.225294 -1710.577126 11730.886581 -4908.127886
## 277 278 279 280 281
## 1414.810375 -685.331352 7541.582941 -5385.051956 -3041.429486
## 282 283 284 285 286
## -11560.872994 -2935.906328 18395.421344 7472.893211 2404.863144
## 287 288 289 290 291
## -961.228447 579.570568 6073.302689 6543.667920 -19124.988049
## 292 293 294 295 296
## -11429.097413 -8374.947666 9436.367002 2813.560254 -1446.454886
## 297 298 299 300 301
## 27138.806534 9719.204956 4530.925109 9142.462529 2462.305291
## 302 303 304 305 306
## -1423.095207 7521.211793 -24683.795770 -3832.351597 -455.532580
## 307 308 309 310 311
## -7243.491863 -4220.593714 2698.187119 -9435.225956 -3441.289314
## 312 313 314 315 316
## -8387.410871 1389.908630 -3336.355010 1869.292564 -4274.093733
## 317 318 319 320 321
## 27262.342151 -1026.467741 2993.549326 10523.712554 5248.762832
## 322 323 324 325 326
## 32028.254848 4658.581674 -21385.116731 1437.835469 761.014380
## 327 328 329 330 331
## -6807.460639 -2043.711086 -33563.592806 745.755872 -2443.138796
## 332 333 334 335 336
## -227.485297 -3304.808006 3956.872512 -585.859106 -7103.056778
## 337 338 339 340 341
## -3244.138388 -2312.601577 -7797.993477 3756.027071 -1490.836818
## 342 343 344 345 346
## -1859.194081 -1115.386391 52.169212 349.777334 -1758.724018
## 347 348 349 350 351
## -9586.652357 -13320.118237 2241.856952 -4413.199323 -3742.100372
## 352 353 354 355 356
## -6060.223282 1682.393458 1295.496997 2646.327499 -3895.893778
## 357 358 359 360 361
## -639.959127 546.208426 6870.843317 96.812683 -223.593147
## 362 363 364 365 366
## 2394.207956 -2952.838793 -1069.029323 -8932.322018 -4777.488263
## 367 368 369 370 371
## -6347.248624 -5062.376585 -7351.190005 4938.720014 259.857463
## 372 373 374 375 376
## 6997.091999 -7799.138270 -2396.215631 -3517.132198 -2587.494033
## 377 378 379 380 381
## -12573.980888 1833.301916 -10724.611447 5641.415142 9240.810726
## 382 383 384 385 386
## 2976.580564 -2569.830932 1438.925750 6565.354605 11196.103825
## 387 388 389 390 391
## -6072.491227 -5607.985211 -379.938093 8339.552255 1549.997049
## 392 393 394 395 396
## 10949.543794 -10201.277878 2500.246350 427.849473 277.427955
## 397 398 399 400 401
## -937.937102 -840.677785 -14758.996651 8327.374290 -1413.215251
## 402 403 404 405 406
## -1595.824147 6767.230808 -8179.156251 -1500.938193 -2726.806175
## 407 408 409 410 411
## -5999.972981 -3010.405390 -4056.087350 -8878.403012 6047.375900
## 412 413 414 415 416
## 1514.765205 -7514.224980 -7802.750738 14139.848993 3651.054949
## 417 418 419 420 421
## 4299.603025 -8255.723242 -4924.465344 -2758.887484 2672.506796
## 422 423 424 425 426
## -14176.020621 -2890.631318 -9191.926469 2955.619733 6892.061936
## 427 428 429 430 431
## 6444.022063 -4159.617934 -4276.724732 -4861.736696 -1911.060441
## 432 433 434 435 436
## -5831.251435 -6725.357065 -6025.323108 -1451.603174 -912.531790
## 437 438 439 440 441
## -5048.521323 2520.083925 4751.803871 -5179.381712 -2264.226294
## 442 443 444 445 446
## 1471.861420 -3958.344275 2725.388507 -6710.429669 -12217.493798
## 447 448 449 450 451
## -4569.336669 9595.948557 -2139.858554 4648.432915 -6005.598450
## 452 453 454 455 456
## -1235.941445 269.101945 2903.121026 -12411.635373 3278.308917
## 457 458 459 460 461
## -6816.531961 6430.997678 2882.095559 2357.610990 -4010.260817
## 462 463 464 465 466
## 1944.045484 -168.982486 1629.229278 -695.161451 3177.872258
## 467 468 469 470 471
## -2828.181834 5629.489375 -7144.086007 -3130.487718 -2356.962682
## 472 473 474 475 476
## -4805.749940 2874.171787 7657.288863 -6195.498045 1334.459809
## 477 478 479 480 481
## -6336.646206 -2974.603983 1891.591007 -13063.510792 -9835.032438
## 482 483 484 485 486
## -1248.429299 -32.981665 -1027.356415 -1413.500672 -9660.584852
## 487 488 489 490 491
## 11051.248829 6129.714737 7280.367779 -5612.852382 5215.227961
## 492 493 494 495 496
## 9115.058670 5835.005809 -13714.942601 -10740.288059 -3567.219448
## 497 498 499 500 501
## -1219.694729 -637.591299 -7741.322121 525.215037 4192.390674
## 502 503 504 505 506
## 5388.458775 512.083376 -71.914671 -7392.617072 447.421506
## 507 508 509 510 511
## -5176.680183 1723.262920 -1418.390098 -8277.801880 -690.775196
## 512 513 514 515 516
## -2767.573961 -675.964788 1239.296916 -9600.637939 -7835.863233
## 517 518 519 520 521
## 24239.443749 9661.889334 5672.759775 -5564.364007 2598.339160
## 522 523 524 525 526
## 16809.880113 11193.961074 -24468.249641 -5259.623211 -3907.492442
## 527 528 529 530 531
## 4415.334423 -534.298224 -11277.725040 4265.620287 13761.090761
## 532 533 534 535 536
## -5187.762298 4187.825295 5351.055881 -2015.870190 -4753.478369
## 537 538 539 540 541
## -7262.278405 -2255.057262 8177.024797 -55.772463 -8323.022767
## 542 543 544 545 546
## 1671.845670 -753.000604 214.835489 -11184.722952 -11168.284379
## 547 548 549 550 551
## 1976.629389 6921.087875 -1438.096416 718.048319 -7847.395853
## 552 553 554 555 556
## 8468.132457 766.459062 -12090.864995 9065.020554 8513.270891
## 557 558 559 560 561
## -86.344102 4668.078928 -3779.693653 13921.501958 21251.029249
## 562 563 564 565 566
## -6801.982582 -9972.766349 6536.935165 -34.930183 3201.440925
## 567 568 569 570 571
## -7637.101790 -17525.463113 6528.679775 6271.322045 1712.864239
## 572 573 574 575 576
## 2904.623429 1567.231243 -2370.186629 14526.522710 -9900.330032
## 577 578 579 580 581
## -6446.244138 8540.553572 2651.290368 -6760.276442 7327.964788
## 582 583 584 585 586
## -4010.623150 -2964.563361 15529.110736 -14738.654412 8258.251087
## 587 588 589 590 591
## -134.087180 -6411.924340 -921.805523 89.895345 -10814.628676
## 592 593 594 595 596
## 1682.713746 -7269.010775 2971.604465 8752.414440 -7655.830795
## 597 598 599 600 601
## 5728.316135 2584.088451 6693.076383 -3380.983068 5976.184430
## 602 603 604 605 606
## -8495.350384 2098.537755 1104.936385 2969.931313 1316.067839
## 607 608 609 610 611
## 215.600681 -5990.025593 7923.574921 -1367.474315 -2748.184446
## 612 613 614 615 616
## -3612.523393 -8370.280709 11851.868397 4777.591436 -9491.324310
## 617 618 619 620 621
## 11492.112665 5868.055015 -5774.589797 26188.607558 -13096.912776
## 622 623 624 625 626
## -6982.442417 2990.487507 -4334.185946 -10745.382767 11188.367334
## 627 628 629 630 631
## -21790.290997 -2482.786487 8610.439057 11030.433195 -1703.482606
## 632 633 634 635 636
## 33141.729172 -6856.688759 5488.372143 5159.230270 -2517.064878
## 637 638 639 640 641
## -5571.531606 -2134.773082 -12610.686289 -2368.750309 -2003.934886
## 642 643 644 645 646
## -2631.590629 -2961.136871 1720.453447 4326.070101 16840.772154
## 647 648 649 650 651
## 18363.717034 628.926605 4543.634655 10360.059434 19872.033497
## 652 653 654 655 656
## 411.071340 -28374.042188 -1525.090726 -2460.241276 1716.215460
## 657 658 659 660 661
## -3343.753519 -10755.267801 1573.033520 4128.524246 -1119.206262
## 662 663 664 665 666
## 12924.713844 1208.941106 1662.817029 -11842.486904 1272.328961
## 667 668 669 670 671
## 1077.021367 -5278.522339 -7503.225392 1998.450792 -3788.831993
## 672 673 674 675 676
## 2606.687886 -3457.168224 -9405.885783 -8349.986990 -3004.393819
## 677 678 679 680 681
## 144.962249 2809.502812 659.361327 -3888.076295 -1863.185333
## 682 683 684 685 686
## -1373.085893 -8298.821275 4611.046744 -2301.264225 -1455.534140
## 687 688 689 690 691
## 529.130403 10788.668012 9749.313306 10495.971947 -9815.080801
## 692 693 694 695 696
## -3669.063393 -3240.889149 5780.518732 -10491.762489 -7985.798457
## 697 698 699 700 701
## -8664.975240 -6309.067354 -4764.229744 3061.187333 -4439.781405
## 702 703 704 705 706
## -1931.278913 4186.832085 31053.904008 9409.848741 23330.446368
## 707 708 709 710 711
## 1548.821636 8204.844260 22806.032921 6435.733206 -18317.801599
## 712 713 714 715 716
## 4745.024396 -5517.843949 -162.394107 422.072935 -17321.619566
## 717 718 719 720 721
## -5298.250621 3306.322005 -3046.210419 -13008.034566 4263.493960
## 722 723 724 725 726
## -5579.394828 723.750022 -3956.319207 -12466.412474 1359.765742
## 727 728 729 730 731
## -1879.944249 -9791.353666 17262.799403 1746.559358 -2754.294511
## 732 733 734 735 736
## 5686.109129 -8666.599841 -749.957930 8111.445976 -15388.225337
## 737 738 739 740 741
## -5930.950519 7392.540646 -4810.817624 138.418894 1803.351438
## 742 743 744 745 746
## -1983.724564 -5194.856323 6390.227190 -6306.050011 22673.824549
## 747 748 749 750 751
## 7777.098262 -2002.932936 -7339.161336 23375.438483 -4354.261495
## 752 753 754 755 756
## 1342.364319 -14474.172355 56074.294127 26837.909544 15000.490034
## 757 758 759 760 761
## -10740.498840 10534.495067 7241.986793 5739.555698 -46455.560507
## 762 763 764 765 766
## -16190.377632 961.240891 -2524.615819 -3463.725762 122839.885363
## 767 768 769 770 771
## 19222.211095 43630.674588 22479.827771 11969.952312 15746.931981
## 772 773 774 775 776
## 25629.534221 -98911.074682 -6783.211701 -35851.066289 1742.280646
## 777 778 779 780 781
## -1226.473726 3397.934468 -7416.226050 -1442.351444 -1942.856554
## 782 783 784 785 786
## 3426.986415 -7155.803432 -2233.354556 3923.229504 2265.716590
## 787 788 789 790 791
## -2760.481368 -4047.125602 1741.465509 2854.358702 -52.495250
## 792 793 794 795 796
## -6704.391970 -5779.294276 -1141.179212 -1257.373839 -7811.804446
## 797 798 799 800 801
## -2344.668502 -3259.079742 -2656.035699 10733.845810 2242.465103
## 802 803 804 805 806
## 7079.139928 2918.397072 -5454.553079 8185.499907 9867.779363
## 807 808 809 810 811
## -10614.320074 -7399.825202 -7503.056531 3012.814963 4198.455032
## 812 813 814 815 816
## -2268.521790 -14162.327072 -4098.702891 6272.692275 8244.489970
## 817 818
## -9671.986930 -7742.152242
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17246.16 20096.81 24355.98 24073.60 26429.84 23759.24 24476.43 19702.73
## 10 11 12 13 14 15 16 17
## 19438.97 16778.49 17556.90 14282.31 14333.54 14999.00 16697.65 15015.62
## 18 19 20 21 22 23 24 25
## 16051.55 15424.32 22515.40 21598.29 21077.52 22969.83 22294.99 22948.34
## 26 27 28 29 30 31 32 33
## 24796.54 18717.53 20445.79 28292.66 28350.39 28022.90 25649.43 27053.57
## 34 35 36 37 38 39 40 41
## 30901.29 31250.14 32661.04 30168.61 34142.76 37354.75 34407.27 31214.27
## 42 43 44 45 46 47 48 49
## 30060.64 20628.70 28156.58 30596.03 31686.57 38533.00 38026.96 42694.83
## 50 51 52 53 54 55 56 57
## 46936.26 39625.28 34187.12 29203.76 22339.83 28637.50 25213.97 21507.07
## 58 59 60 61 62 63 64 65
## 25920.79 27181.88 27479.25 27891.57 23757.48 40368.96 42215.16 37455.34
## 66 67 68 69 70 71 72 73
## 41667.40 46589.03 57270.19 55281.55 40506.77 38009.60 41031.17 35324.38
## 74 75 76 77 78 79 80 81
## 30771.08 21463.21 24668.58 20589.34 22677.94 17567.26 19585.14 18810.73
## 82 83 84 85 86 87 88 89
## 17837.27 15903.85 17183.18 20795.62 25218.72 26209.80 26234.78 26852.27
## 90 91 92 93 94 95 96 97
## 30982.37 29811.50 30812.07 28874.15 28072.99 28441.59 28848.66 22418.30
## 98 99 100 101 102 103 104 105
## 25437.00 18459.00 17313.75 15352.09 15652.21 16330.42 20808.49 19890.89
## 106 107 108 109 110 111 112 113
## 23406.25 23195.97 24874.12 27754.38 25249.46 21688.61 21964.37 24613.86
## 114 115 116 117 118 119 120 121
## 35491.55 33682.21 35521.92 38523.88 40482.16 38167.78 32982.51 29319.46
## 122 123 124 125 126 127 128 129
## 31404.81 29682.68 30865.67 38474.65 38116.68 37177.08 34036.81 35820.16
## 130 131 132 133 134 135 136 137
## 41224.59 40664.21 31855.24 33121.57 36315.20 32721.50 31106.82 30190.71
## 138 139 140 141 142 143 144 145
## 26743.57 28159.71 27929.06 25612.55 27639.43 26262.52 19856.90 22891.10
## 146 147 148 149 150 151 152 153
## 20715.16 23695.88 24236.52 25828.83 26011.13 27667.29 28969.61 32005.43
## 154 155 156 157 158 159 160 161
## 27470.13 26732.20 24284.50 30185.71 41655.29 39983.51 37345.27 42371.61
## 162 163 164 165 166 167 168 169
## 43781.88 47201.91 42662.36 37963.88 43395.67 59717.53 61932.32 60344.51
## 170 171 172 173 174 175 176 177
## 57166.37 55563.69 58269.52 57292.44 49575.26 52402.17 56149.31 56185.54
## 178 179 180 181 182 183 184 185
## 63322.98 53813.81 50560.84 41358.42 32887.56 36400.05 46515.70 45971.06
## 186 187 188 189 190 191 192 193
## 51931.00 57682.90 68478.88 73768.11 67455.20 67648.87 74727.70 70398.93
## 194 195 196 197 198 199 200 201
## 65916.36 55147.24 49170.19 50596.20 46185.59 38344.97 44777.45 43002.71
## 202 203 204 205 206 207 208 209
## 42640.24 43119.16 49921.36 58821.29 58450.45 60177.33 61835.12 65630.86
## 210 211 212 213 214 215 216 217
## 75108.78 67182.07 55356.90 49959.22 40944.72 37898.78 41016.19 31024.36
## 218 219 220 221 222 223 224 225
## 48018.31 55347.90 56250.89 79043.72 86547.74 88553.38 96153.86 87093.88
## 226 227 228 229 230 231 232 233
## 81085.09 80666.52 77312.46 76472.95 81215.66 82581.57 77010.34 72217.53
## 234 235 236 237 238 239 240 241
## 77877.97 64509.72 56536.64 48477.61 40079.10 44276.04 46431.36 39874.18
## 242 243 244 245 246 247 248 249
## 33546.15 43840.98 38080.95 42021.38 34275.97 32980.27 36639.95 39467.23
## 250 251 252 253 254 255 256 257
## 30285.68 36230.87 40036.86 45237.88 47959.46 47451.43 57764.74 75282.96
## 258 259 260 261 262 263 264 265
## 75097.79 68400.42 69884.03 66100.28 67548.16 61298.35 50560.09 46582.67
## 266 267 268 269 270 271 272 273
## 46792.33 42894.38 51709.09 47977.70 52129.41 50243.72 54315.97 54612.35
## 274 275 276 277 278 279 280 281
## 60637.01 58268.40 67952.99 61870.48 62080.76 60427.85 66177.62 59900.57
## 282 283 284 285 286 287 288 289
## 56460.30 46000.05 44394.86 61647.82 67184.57 67594.51 65009.00 64095.27
## 290 291 292 293 294 295 296 297
## 68101.05 72015.99 52989.67 43079.80 37083.63 47417.44 50663.17 49776.05
## 298 299 300 301 302 303 304 305
## 74001.51 79954.07 80622.54 85240.55 83436.95 78461.22 81932.22 56800.78
## 306 307 308 309 310 311 312 313
## 53057.39 52736.78 46519.45 43725.53 47333.23 39876.43 38596.98 33151.95
## 314 315 316 317 318 319 320 321
## 36941.07 36121.42 39957.52 37939.51 63757.04 61595.59 63221.14 71228.95
## 322 323 324 325 326 327 328 329
## 73619.17 99131.70 97507.40 73308.31 72104.70 70460.03 62402.00 59520.74
## 330 331 332 333 334 335 336 337
## 29432.67 33124.71 33564.77 35887.52 35227.56 41001.57 42078.49 37320.28
## 338 339 340 341 342 343 344 345
## 36533.74 36660.56 31973.83 37980.12 38644.34 38903.10 39779.97 41568.08
## 346 347 348 349 350 351 352 353
## 43392.30 43143.65 36079.69 26636.00 31987.20 30846.81 30436.37 28049.89
## 354 355 356 357 358 359 360 361
## 32734.50 36493.39 40962.47 39149.24 40411.08 42552.16 49956.47 50507.74
## 362 363 364 365 366 367 368 369
## 50709.65 53175.84 50656.17 50100.04 42736.20 39929.53 36101.81 33877.76
## 370 371 372 373 374 375 376 377
## 29930.71 37227.57 39517.34 47412.57 41376.79 40823.28 39358.78 38890.98
## 378 379 380 381 382 383 384 385
## 29747.41 34351.18 27394.30 35623.76 45969.56 49539.40 47810.65 49804.79
## 386 387 388 389 390 391 392 393
## 56032.61 65529.78 58732.70 53194.08 52922.45 60311.15 60835.17 69514.56
## 394 395 396 397 398 399 400 401
## 58606.75 60175.58 59735.14 59218.37 57703.39 56463.43 43205.63 51801.93
## 402 403 404 405 406 407 408 409
## 50801.11 49766.05 56175.30 48708.51 48018.81 46343.40 42015.26 40844.52
## 410 411 412 413 414 415 416 417
## 38905.97 32992.77 40875.38 43805.37 38471.04 33553.15 48443.37 52292.97
## 418 419 420 421 422 423 424 425
## 56227.15 48686.89 45005.60 43679.92 47270.88 35675.49 35404.36 29655.95
## 426 427 428 429 430 431 432 433
## 35252.80 43590.84 50491.62 47253.01 44318.02 41239.35 41127.39 37600.79
## 434 435 436 437 438 439 440 441
## 33734.32 30964.89 32542.96 34394.66 32396.77 37269.05 43482.38 40230.65
## 442 443 444 445 446 447 448 449
## 39936.28 42946.49 40829.90 44824.43 40065.35 31086.34 29922.34 41293.57
## 450 451 452 453 454 455 456 457
## 40974.71 46633.03 42263.66 42613.76 44236.31 47959.21 37820.69 42676.10
## 458 459 460 461 462 463 464 465
## 38093.57 45672.19 49196.67 51820.55 48545.95 50889.70 51091.49 52840.73
## 466 467 468 469 470 471 472 473
## 52337.70 55285.18 52610.08 57667.66 50919.06 48526.96 47111.32 43731.40
## 474 475 476 477 478 479 480 481
## 47492.28 54965.07 49384.97 51090.36 45872.60 44249.55 47086.08 36486.89
## 482 483 484 485 486 487 488 489
## 30040.29 31911.98 34612.07 36103.93 37071.01 30703.75 43249.86 49918.49
## 490 491 492 493 494 495 496 497
## 56757.42 51462.20 56301.37 63944.71 67760.94 53999.86 44565.79 42588.27
## 498 499 500 501 502 503 504 505
## 42911.88 43704.04 38183.78 40585.75 45893.97 51582.77 52293.34 52404.05
## 506 507 508 509 510 511 512 513
## 46098.01 47439.68 43694.17 46453.10 46118.37 39826.20 40958.72 40132.82
## 514 515 516 517 518 519 520 521
## 41239.85 43883.21 36714.29 31987.70 55907.54 64078.53 67736.08 61106.80
## 522 523 524 525 526 527 528 529
## 62447.98 76050.75 83036.25 57954.91 52818.49 49508.67 53893.16 53398.87
## 530 531 532 533 534 535 536 537
## 43570.09 48568.19 61244.62 55758.60 59160.52 63153.30 60202.19 55226.71
## 538 539 540 541 542 543 544 545
## 48680.77 47334.98 55282.06 55032.17 47582.87 49809.29 49635.74 50330.44
## 546 547 548 549 550 551 552 553
## 40967.71 32793.23 37140.48 45267.24 45063.95 46771.97 40774.30 49798.54
## 554 555 556 557 558 559 560 561
## 50955.29 40721.69 50274.59 58147.20 57511.35 61113.55 56875.50 68650.69
## 562 563 564 565 566 567 568 569
## 85360.13 75438.77 63988.06 68412.79 66534.84 67722.96 59282.46 43251.61
## 570 571 572 573 574 575 576 577
## 50268.96 56181.42 57365.66 59443.77 60091.62 57214.48 69476.33 58836.53
## 578 579 580 581 582 583 584 585
## 52551.73 60162.71 61668.56 54754.04 61028.34 56598.99 53639.89 67226.80
## 586 587 588 589 590 591 592 593
## 52637.32 59990.66 59081.92 52796.38 52100.68 52377.06 43081.43 45881.73
## 594 595 596 597 598 599 600 601
## 40501.54 44752.59 53526.69 46849.68 52715.91 55096.64 60772.70 56926.10
## 602 603 604 605 606 607 608 609
## 61745.78 53304.03 55186.35 55963.64 58274.65 58849.40 58389.60 52559.85
## 610 611 612 613 614 615 616 617
## 59630.19 57687.90 54781.52 51483.57 44437.85 55962.27 59854.47 50778.74
## 618 619 620 621 622 623 624 625
## 61193.52 65383.59 58865.39 81120.20 66224.73 58544.66 60550.04 55897.67
## 626 627 628 629 630 631 632 633
## 46221.20 56941.72 37474.22 37334.28 46914.28 57409.77 55451.99 84216.12
## 634 635 636 637 638 639 640 641
## 74390.34 76593.77 78233.06 72952.96 65663.34 62293.54 50183.75 48550.08
## 642 643 644 645 646 647 648 649
## 47440.30 45920.71 44303.40 46983.50 51606.51 66595.57 81037.36 78157.22
## 650 651 652 653 654 655 656 657
## 79062.08 84940.68 98401.64 93153.90 63387.95 60836.67 57787.36 58773.18
## 658 659 660 661 662 663 664 665
## 55209.84 45610.97 47998.19 52321.21 51512.43 63088.20 62965.75 63255.63
## 666 667 668 669 670 671 672 673
## 51697.10 53058.26 54077.95 49411.08 43383.55 46422.12 44018.03 47509.03
## 674 675 676 677 678 679 680 681
## 45258.74 38087.70 32739.25 32736.75 35489.07 40226.78 42489.93 40492.04
## 682 683 684 685 686 687 688 689
## 40515.66 40964.96 35300.52 41637.55 41134.39 41434.01 43431.90 54152.54
## 690 691 692 693 694 695 696 697
## 62620.03 70678.94 59962.92 55965.89 52844.48 58004.76 48285.94 41977.40
## 698 699 700 701 702 703 704 705
## 35865.78 32580.94 31059.10 36572.35 34833.85 35507.31 41447.38 70141.29
## 706 707 708 709 710 711 712 713
## 76307.27 93875.46 90190.30 92788.68 107831.84 106671.09 84005.83 84353.56
## 714 715 716 717 718 719 720 721
## 75681.54 72780.78 70754.91 53463.96 48856.82 52353.07 49854.89 38957.08
## 722 723 724 725 726 727 728 729
## 44531.68 40798.54 43046.32 40918.98 31615.23 35570.66 36196.64 29824.63
## 730 731 732 733 734 735 736 737
## 47913.73 50164.01 48195.61 53856.17 46253.82 46528.70 54519.51 40955.09
## 738 739 740 741 742 743 744 745
## 37362.89 45874.10 42644.87 44149.22 46921.15 46033.28 42448.20 49445.19
## 746 747 748 749 750 751 752 753
## 44460.46 65447.19 70773.65 66878.45 58804.42 78606.40 71672.64 70590.60
## 754 755 756 757 758 759 760 761
## 55810.71 104587.23 121677.51 126271.78 107776.36 110207.44 109454.02 107480.99
## 762 763 764 765 766 767 768 769
## 60104.23 45138.04 47049.47 45672.44 43646.69 152343.07 156785.04 182018.32
## 770 771 772 773 774 775 776 777
## 185588.90 179519.64 177514.75 184404.79 81504.78 72083.21 38419.43 41856.33
## 778 779 780 781 782 783 784 785
## 42265.78 46668.51 41060.92 41381.29 41223.73 45782.52 40513.78 40210.91
## 786 787 788 789 790 791 792 793
## 45330.71 48358.91 46611.41 43957.68 46699.50 50070.92 50477.25 45014.72
## 794 795 796 797 798 799 800 801
## 41046.18 41631.80 42042.38 36668.81 36750.65 36022.46 35913.01 47528.39
## 802 803 804 805 806 807 808 809
## 50260.72 56880.75 59031.70 53589.79 60760.08 68502.75 57360.54 50426.77
## 810 811 812 813 814 815 816 817
## 44272.04 48086.40 52459.52 50628.18 38623.85 36926.45 44512.94 52872.84
## 818
## 44514.44
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.811
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 4.931073 0.7705721 3.888013
## t2* 2636.027643 166.0158776 873.069298
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.055872 4.851715 13.10195
## 2 lag_depvar 1596.648803 2686.238082 4456.17183Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Sat Mar 1 16:58:26 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Sat Mar 1 16:58:35 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Sat Mar 1 16:58:44 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Sat Mar 1 16:58:53 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Sat Mar 1 16:59:02 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Sat Mar 1 16:59:11 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Sat Mar 1 16:59:20 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Sat Mar 1 16:59:29 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Sat Mar 1 16:59:39 2025
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Sat Mar 1 16:59:48 2025
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_25<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2025",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2020",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_25 %>%
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))| Item | 2025 | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|---|
| Agua | 0.0000 | 6.993667 | 5.195333 | 5.410333 | 5.849167 | 9.93775 |
| Comida | 258.8850 | 326.890000 | 366.009167 | 312.386750 | 317.896583 | 392.93367 |
| Comunicaciones | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electricidad | 59.9440 | 83.582750 | 38.104750 | 47.072333 | 29.523000 | 20.60458 |
| Enceres | 0.0000 | 23.989000 | 18.259750 | 24.219750 | 14.801167 | 39.01200 |
| Farmacia | 0.0000 | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 14.03675 |
| Gas/Bencina | 0.0000 | 44.292667 | 42.636000 | 45.575000 | 13.583667 | 17.25833 |
| Diosi | 36.9995 | 33.319583 | 55.804250 | 31.180667 | 52.687833 | 37.12133 |
| donaciones/regalos | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electrodomésticos/ Mantención casa | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| VTR | 10.9950 | 18.326667 | 12.829167 | 25.156667 | 19.086917 | 19.11375 |
| Netflix | 0.0000 | 1.391417 | 8.713833 | 7.151583 | 7.028750 | 8.24725 |
| Otros | 0.0000 | 76.164000 | 5.481667 | 5.000000 | 0.000000 | 0.00000 |
| Total | 366.8235 | 614.949750 | 563.738000 | 505.988083 | 474.453167 | 558.26542 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")
tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 54 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2624, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)# Configurar API Key
nixtlar::nixtla_set_api_key(Sys.getenv("API_NIXTLA"))## API key has been set for the current session.try(nixtlar::nixtla_set_api_key(Sys.getenv("NIXTLA")))## API key has been set for the current session.# Preparar datos en formato requerido por TimeGPT
uf_timegpt <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y)
# Realizar pronóstico con TimeGPT
timegpt_fcst <- nixtlar::nixtla_client_forecast(
uf_timegpt,
h = 298, # 298 días a pronosticar
freq = "D", # Frecuencia diaria
add_history = TRUE, # Incluir datos históricos en el output
level = c(80,95),
model= "timegpt-1-long-horizon",
clean_ex_first = TRUE
)## The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.# 1. Convertir 'ds' a fecha en ambas tablas
uf_timegpt <- uf_timegpt %>%
mutate(ds = as.Date(ds))
timegpt_fcst <- timegpt_fcst %>%
mutate(ds = as.Date(ds))
# 2. Combinar los datos históricos y el pronóstico
full_data <- bind_rows(
uf_timegpt %>% mutate(type = "Histórico"),
timegpt_fcst %>% mutate(type = "Pronóstico")
)
# Visualizar resultados
ggplot(full_data, aes(x = ds, y = TimeGPT)) +
# Intervalo de confianza del 95%
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
# Intervalo de confianza del 80%
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
# Línea histórica
geom_line(data = filter(full_data, type == "Histórico"),
aes(color = "Histórico"), size = 1) +
# Línea de pronóstico
geom_line(data = filter(full_data, type == "Pronóstico"),
aes(color = "Pronóstico"), size = 1) +
# Línea vertical separadora
geom_vline(xintercept = max(filter(full_data, type == "Histórico")$ds),
linetype = "dashed", color = "red", size = 0.8) +
# Configuración del eje x
scale_x_date(
date_breaks = "3 months", # Reduce la frecuencia de las etiquetas
date_labels = "%b %Y", # Formato de etiquetas (mes y año)
) +
# Configuración del eje y
scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
# Configuración de colores
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
# Títulos y subtítulos
labs(
title = "Pronóstico de Serie Temporal con TimeGPT",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Valor",
color = "Leyenda"
) +
# Tema y estilos
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)## Warning: Removed 2624 rows containing missing values or values outside the scale range
## (`geom_line()`).
library(prophet)## Warning: package 'prophet' was built under R version 4.4.2## Loading required package: Rcpp## Warning: package 'Rcpp' was built under R version 4.4.2## Loading required package: rlang## Warning: package 'rlang' was built under R version 4.4.2##
## Attaching package: 'rlang'## The following objects are masked from 'package:purrr':
##
## %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
## flatten_raw, invoke, splice## The following object is masked from 'package:sparklyr':
##
## invoke## The following object is masked from 'package:data.table':
##
## := model <- prophet(
cbind.data.frame(ds= as.Date(uf_timegpt$ds), y=uf_timegpt$y),
# Trend flexibility
growth = "linear",
changepoint.prior.scale = 0.05, # Reduced for smoother trend
n.changepoints = 50, # Increased from default 25
# Seasonality
yearly.seasonality = TRUE,
weekly.seasonality = TRUE,
daily.seasonality = FALSE, # Disabled for daily data
seasonality.mode = "additive",
seasonality.prior.scale = 15, # Increased to capture stronger seasonality
# Holidays (if applicable)
# holidays = generated_holidays # Create with add_country_holidays()
# Uncertainty intervals
interval.width = 0.95,
uncertainty.samples = 1000
)
future <- make_future_dataframe(model, periods = 298, include_history = T)
forecast <- predict(model, future)
forecast <- forecast[, c("ds", "yhat", "yhat_lower", "yhat_upper")]
forecast$pred <- ifelse(forecast$ds > max(uf_timegpt$ds), 1,0)## Warning in check_tzones(e1, e2): 'tzone' attributes are inconsistentforecast$ds <- as.Date(forecast$ds)
ggplot(forecast, aes(x = ds, y = yhat)) +
geom_ribbon(aes(ymin = yhat_lower, ymax = yhat_upper),
fill = "#9ecae1", alpha = 0.4) +
geom_line(color = "#08519c", linewidth = 0.8) +
geom_vline(xintercept = max(uf_timegpt$ds), color = "red", linetype = "dashed", linewidth=1) +
scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
scale_y_continuous(labels = scales::comma) +
labs(title = "Valores predichos (95%IC)",
# subtitle = "March 10, 2025 - May 7, 2025",
x = "Fecha",
y = "Valor",
# caption = "Source: Prophet Forecast Model"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
plot.subtitle = element_text(color = "gray50"),
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
plot.caption = element_text(color = "gray30")
)
La proyección de la UF a 298 días más 2025-03-09 00:04:58 sería de: 26.921 pesos// Percentil 95% más alto proyectado: 35.097,94
Según TimeGPT: La proyección de la UF a 298 días más 2026-01-01 sería de: 39.556,3 pesos// Percentil 80% más alto proyectado: 39.946,4 pesos// Percentil 95% más alto proyectado: 41.030,12
Según prophet: La proyección de la UF a 298 días más 2026-01-01 sería de: 38.446 pesos// Percentil 95% más alto proyectado: 40.942
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 26537.82 | 26331.66 |
| Lo.80 | 26669.99 | 26493.43 |
| Point.Forecast | 26921.46 | 26799.01 |
| Hi.80 | 31593.20 | 32074.20 |
| Hi.95 | 34386.03 | 34866.72 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(0,1,2)
##
## Coefficients:
## ma1 ma2
## -0.5565 -0.3087
## s.e. 0.1110 0.1126
##
## sigma^2 = 37449: log likelihood = -474.19
## AIC=954.39 AICc=954.75 BIC=961.18paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(0,0,1) errors
##
## Coefficients:
## ma1 intercept xreg
## 0.3642 463.3959 18.3018
## s.e. 0.1004 275.1981 8.4991
##
## sigma^2 = 35487: log likelihood = -477.87
## AIC=963.74 AICc=964.34 BIC=972.85forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 700.1710 | 706.9536 | 742.9974 |
| Lo.80 | 836.1806 | 850.5687 | 836.9886 |
| Point.Forecast | 1093.1089 | 1121.8640 | 1059.2415 |
| Hi.80 | 1350.0372 | 1413.0247 | 1339.9033 |
| Hi.95 | 1486.0468 | 1567.1559 | 1517.1589 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Gastos_casa_mensual_2022$mes_ano <- gsub("marzo", "Mar", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("abril", "Apr", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("mayo", "May", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("junio", "Jun", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("julio", "Jul", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("agosto", "Aug", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("septiembre", "Sep", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("octubre", "Oct", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("noviembre", "Nov", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("diciembre", "Dec", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("enero", "Jan", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("febrero", "Feb", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022<- dplyr::filter(Gastos_casa_mensual_2022, !is.na(Tami))
Gastos_casa_mensual_2022$mes_ano <- parse_date_time(Gastos_casa_mensual_2022$mes_ano, "%b_%Y")
Gastos_casa_mensual_2022$mes_ano <- as.Date(as.character(Gastos_casa_mensual_2022$mes_ano))
Gastos_casa_mensual_2022_timegpt <- Gastos_casa_mensual_2022 %>%
mutate(value = Tami + Andrés) %>%
rename(ds = mes_ano, y = value) %>%
mutate(#ds= format(ds, "%Y-%m"),
unique_id = "1") %>% #it is only one series
select(unique_id, ds, y)
#Convertir la base de UF a mensual
uf_timegpt_my <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y) %>%
mutate(ds = ymd(ds)) %>% # Convert 'ds' to Date
mutate(month = month(ds), year = year(ds)) %>% # Extract month and year
group_by(month, year) %>% # Group by month and year
summarise(average_y = mean(y))%>% # Calculate average y
mutate(ds = as.Date(paste0(year,"-",month, "-01")))%>%
ungroup()%>%
select(ds, uf=average_y)
Gastos_casa_mensual_2022_timegpt_ex<-
Gastos_casa_mensual_2022_timegpt |>
dplyr::left_join(uf_timegpt_my, by=c("ds"="ds"))
#Historical Exogenous Variables: These should be included in the input data immediately following the id_col, ds, and y columns
gastos_timegpt_fcst <- nixtlar::nixtla_client_forecast(
Gastos_casa_mensual_2022_timegpt_ex,
h = 12,
freq = "M", # Monthly frequency
add_history = TRUE,
level = c(80, 95),
model = "timegpt-1",#"timegpt-1-long-horizon",
clean_ex_first = TRUE
)
# Convert 'ds' to Date format in both tables
Gastos_casa_mensual_2022_timegpt_corr <- Gastos_casa_mensual_2022_timegpt %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
gastos_timegpt_fcst <- gastos_timegpt_fcst %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
# Combine historical and forecast data
full_data_gastos <- bind_rows(
Gastos_casa_mensual_2022_timegpt_corr %>% mutate(type = "Histórico"),
gastos_timegpt_fcst %>% mutate(type = "Pronóstico")
)
full_data_gastos |>
dplyr::mutate(y= ifelse(is.na(y),TimeGPT, y)) |>
# Visualize results
ggplot(aes(x = ds, y = y)) +
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
geom_line(aes(color = type), linewidth = 1.5) +
geom_vline(xintercept = max(filter(full_data_gastos, type == "Histórico")$ds),
linetype = "dashed", color = "red", linewidth = 0.8) +
scale_x_date(
date_breaks = "3 months",
date_labels = "%b %Y"
) +
scale_y_continuous(
name = "Gastos Totales",
labels = scales::comma,
breaks = pretty(full_data_gastos$y, n = 10),
expand = expansion(mult = c(0.05, 0.05))
) +
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
labs(
title = "Pronóstico de Gastos Mensuales (TimeGPT, ajustando por UF promedio mensual)",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Gastos Totales",
color = "Leyenda"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
## system code page: 65001
##
## time zone: UTC
## tzcode source: internal
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] prophet_1.0 rlang_1.1.5 Rcpp_1.0.14
## [4] CausalImpact_1.3.0 bsts_0.9.10 BoomSpikeSlab_1.2.6
## [7] Boom_0.9.15 scales_1.3.0 ggiraph_0.8.12
## [10] tidytext_0.4.2 DT_0.33 janitor_2.2.1
## [13] autoplotly_0.1.4 rvest_1.0.4 plotly_4.10.4
## [16] xts_0.14.1 forecast_8.23.0 wordcloud_2.6
## [19] RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-16
## [22] NLP_0.3-2 tsibble_1.1.6 lubridate_1.9.4
## [25] forcats_1.0.0 dplyr_1.1.4 purrr_1.0.4
## [28] tidyr_1.3.1 tibble_3.2.1 tidyverse_2.0.0
## [31] gsynth_1.2.1 sjPlot_2.8.17 lattice_0.22-6
## [34] GGally_2.2.1 ggplot2_3.5.1 gridExtra_2.3
## [37] plotrix_3.8-4 sparklyr_1.8.6 httr_1.4.7
## [40] readxl_1.4.4 zoo_1.8-13 stringr_1.5.1
## [43] stringi_1.8.4 DataExplorer_0.8.3 data.table_1.16.4
## [46] reshape2_1.4.4 fUnitRoots_4040.81 plyr_1.8.9
## [49] readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-9 cellranger_1.1.0 datawizard_1.0.0
## [4] httr2_1.1.0 lifecycle_1.0.4 StanHeaders_2.32.10
## [7] doParallel_1.0.17 globals_0.16.3 vroom_1.6.5
## [10] MASS_7.3-60.2 insight_1.0.2 crosstalk_1.2.1
## [13] magrittr_2.0.3 sass_0.4.9 rmarkdown_2.29
## [16] jquerylib_0.1.4 yaml_2.3.10 fracdiff_1.5-3
## [19] doRNG_1.8.6.1 askpass_1.2.1 pkgbuild_1.4.6
## [22] DBI_1.2.3 abind_1.4-8 quadprog_1.5-8
## [25] nnet_7.3-19 rappdirs_0.3.3 sandwich_3.1-1
## [28] inline_0.3.21 tokenizers_0.3.0 listenv_0.9.1
## [31] anytime_0.3.11 performance_0.13.0 spatial_7.3-17
## [34] parallelly_1.42.0 codetools_0.2-20 xml2_1.3.6
## [37] tidyselect_1.2.1 ggeffects_2.2.0 farver_2.1.2
## [40] urca_1.3-4 its.analysis_1.6.0 matrixStats_1.5.0
## [43] stats4_4.4.0 jsonlite_1.8.9 ellipsis_0.3.2
## [46] Formula_1.2-5 iterators_1.0.14 systemfonts_1.2.1
## [49] foreach_1.5.2 tools_4.4.0 glue_1.8.0
## [52] xfun_0.50 TTR_0.24.4 ggfortify_0.4.17
## [55] loo_2.8.0 withr_3.0.2 timeSeries_4041.111
## [58] fastmap_1.2.0 boot_1.3-30 openssl_2.3.2
## [61] caTools_1.18.3 digest_0.6.37 timechange_0.3.0
## [64] R6_2.6.1 lfe_3.1.1 colorspace_2.1-1
## [67] networkD3_0.4 gtools_3.9.5 generics_0.1.3
## [70] htmlwidgets_1.6.4 ggstats_0.8.0 pkgconfig_2.0.3
## [73] gtable_0.3.6 timeDate_4041.110 lmtest_0.9-40
## [76] selectr_0.4-2 janeaustenr_1.0.0 htmltools_0.5.8.1
## [79] carData_3.0-5 tseries_0.10-58 snakecase_0.11.1
## [82] knitr_1.49 rstudioapi_0.17.1 tzdb_0.4.0
## [85] uuid_1.2-1 nlme_3.1-164 curl_6.2.0
## [88] cachem_1.1.0 sjlabelled_1.2.0 KernSmooth_2.23-22
## [91] parallel_4.4.0 fBasics_4041.97 pillar_1.10.1
## [94] vctrs_0.6.5 gplots_3.2.0 slam_0.1-55
## [97] car_3.1-3 dbplyr_2.5.0 xtable_1.8-4
## [100] evaluate_1.0.3 mvtnorm_1.3-3 cli_3.6.4
## [103] compiler_4.4.0 crayon_1.5.3 rngtools_1.5.2
## [106] future.apply_1.11.3 labeling_0.4.3 sjmisc_2.8.10
## [109] rstan_2.32.6 QuickJSR_1.5.1 viridisLite_0.4.2
## [112] assertthat_0.2.1 munsell_0.5.1 lazyeval_0.2.2
## [115] Matrix_1.7-0 sjstats_0.19.0 hms_1.1.3
## [118] bit64_4.6.0-1 future_1.34.0 nixtlar_0.6.2
## [121] extraDistr_1.10.0 igraph_2.1.4 RcppParallel_5.1.10
## [124] bslib_0.9.0 quantmod_0.4.26 bit_4.5.0.1#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))